1. Field of the Invention
The present invention relates to friction estimation and vehicle control.
2. State of the Art
All forces applied to a road vehicle (except aerodynamic forces) during a maneuver are transmitted through its tires. Therefore, knowledge of the capacity of the tire to transmit forces between the tire and road at any instant, under changing road conditions, is highly desirable in order to improve the performance of a vehicle control system. Most traction control and anti-lock brake systems, which regulate traction and braking forces to maximize the friction force between the tire and road surface, have been developed to perform adequately over most road surfaces. However, these methods do not detect the friction force limit until the vehicle is on the verge of exceeding it. More accurate estimation of the instantaneous maximum friction coefficient for the current road conditions is therefore much to be desired to enable the performance of the car to be better optimized for varying road conditions. Increasing interest in autonomous intelligent cruise control, collision avoidance systems and active safety techniques has made road/tire friction estimation an active research topic.
In the absence of commercially available transducers to measure road friction directly, various types of estimation methods have been investigated. Dieckmann, T., Assessment of Road Grip by Way of Measured Wheel Variables, Proc. FISITA, London, June 1992, describes a method which allows very accurate measurement of wheel slip using the standard ABS-wheel-pulse sensors. Using the measured wheel slip, the road surface variation is detected. Gustafsson, F., Slip-Based Tire-Road Friction Estimation, Automatica, 33(6), pp. 1087-1099, 1997, proposes an algorithm for estimating tire-road friction during normal driving using only standard sensors, based on a Kalman filter supported by a change detection algorithm to give reliable and accurate estimates of the slip slope and, at the same time, to be able to follow abrupt changes quickly.
Eichhorn, U. and Roth, J., Prediction and Monitoring of Tyre/Road Friction, Proc. FISITA, London, June 1992, describes the use of optical and noise sensors at the front-end of the tire, and stress and strain sensors inside the tire""s tread, and describes both xe2x80x9ceffective basedxe2x80x9d and xe2x80x9cparameter basedxe2x80x9d road friction methods. Ito, M., Yoshioka, K. and Saji, T., Estimation of Road Surface Conditions Using Wheel Speed Behavior, SAE No. 9438826, describes the use of the applied traction force and the resulting wheel slip difference between the driven and non- driven wheels to estimate the road surface condition without additional sensing devices. Pasterkamp, W. R. and Pacejka, H. B., On line Estimation of Tyre Characteristics for Vehicle Control, Proc. AVEC, No. 9438808, October 1994, describes a on-line estimation method based on the recognition of pneumatic trail through measurements of side force, self-aligning moment, and vertical load of the tire from a measurement system mounted in the vehicle front wheel suspension.
Pal, C., Hagiwara, I., Morishita, S. and Inoue, H., Application of Neural Networks in Real Time Identification of Dynamic Structural Response and Prediction of Road-Friction Coefficient from Steady State Automobile Response, Proc. AVEC, No. 9438826, October 1994, applies a neural-network based identification technique to predict the road friction coefficient based on steady-state vehicle response. Lui, C. and Peng, H. Road Friction Coefficient Estimation for Vehicle Path Prediction, Vehicle System Dynamics Supplement 25, pp. 413-425, 1996, describes the use of a disturbance observer to identify the road surface friction coefficient by using recursive least square and a modified observer method using an anisotropic brush tire model. Kiencke, U. and Daixcex2, A., Estimation of Tyre Friction for Enhanced ABS System, Proc. AVEC, No. 9438790, October 1994, describes estimating the friction coefficient based upon a one-wheel tire model with measurements of the braking pressure.
Daixcex2, A., Model Based Calculation of Friction Curves Between Tyre and Road Surface, Proc. of 4th IEEE Conference on Control Application, pp. 291-295, 1995, presents a model which allows the calculation of the braking force distribution of the car""s braking system using only one pressure sensor, based on calculation of friction curves between tire and road surface. Germann, S., Wurtenberger, M. and Daixcex2, A., Monitoring of the Friction Coefficient Between Tyre and Road Surface, Proc. of 3rd IEEE Conference on Control Application, pp. 613-618, 1994, uses a mathematical model to describe the friction and slip characteristics and to compute the dynamic wheel loads and longitudinal tire force. A recursive least squares estimator determines the friction characteristics using a polynomial approach to the tire-road dynamics.
Some researchers, besides determining the tire/road friction coefficient, have also applied the estimated friction coefficient to IVHS and advanced vehicle control systems (e.g., steering, braking, power train).
A series of papers by L. R. Ray relate to nonlinear estimation of the vehicle state, tire forces and friction coefficient. See (1) Nonlinear Estimation of Vehicle State and Tire Forces, Proc. American Control Conference, pp. 526-530, June 1992; (2) Real-Time Determination of Road Coefficient of Friction for IVHS and Advanced Vehicle Control, Proc. American Control Conference, pp. 2133-2137, June 1995; (3) Nonlinear State and Tire Force Estimation for Advanced Vehicle Control, IEEE Transactions on Control Systems Technology, 3(1), pp. 117-124, March 1995; and (4) Nonlinear Tire Force Estimation and Road Friction Identification: Field Test Results, SAE No. 960181. Paper #1 proposes a method to estimate nonlinear vehicle motion and tire force histories from an incomplete, noise-corrupted measurement set by applying an Extended Kalman Filter (EKF). A nine degree-of-freedom vehicle model and an analytical tire force model are used to simulate true vehicle motion, and a five degree-of-freedom model is used for estimation. In Paper #2, a real-time friction coefficient estimation method is presented that uses EKF and Bayesian decision making. The estimated force histories from Paper #1 are compared with those from a nominal tire force model to determine the most probable road coefficient of friction from a set of hypothesized values. The friction identification and EKF tasks are separately performed so that the EKF state estimates can be used for feedback control while the friction coefficient is identified. A proportional-integral slip control braking system that uses state estimates from the EKF for feedback is described in Paper #3, and field test results are presented in Paper #4.
Wakamatsu, K., Akuta, Y., Ikegaya, M., and Asanuma, N., Adaptive Yaw Rate Feedback 4WS with Tire/Road Friction Coefficient Estimator, Vehicle System Dynamics, 27(N 5-6), pp. 305-326, June 1997, presents an adaptive yaw rate feedback control system for a four-wheel-steering (4WS) vehicle using an estimated friction coefficient, such that the vehicle possesses desirable lateral characteristics even on slippery roads and in critical driving situations. The friction coefficient is estimated in near real-time by identifying the ratio of the actual yaw rate to the linear model yaw rate on high friction roads with a Least Squares Method, and adjusting the ratio using vehicle speed and lateral acceleration. The control system adopts a two degree-of-freedom structure which consists of a feedforward compensator and a feedback control subsystem.
Another paper of interest is Bakker, E., Nyborg, L., and Pacejka, H. B., Tyre Modeling for Use in Vehicle Dynamics Studies, SAE No. 870421.
U.S. Pat. No. 5,747,682 describes a vehicle state estimating apparatus in which operator inputs (steering angle input, accelerator pedal input) are applied to a physical vehicle and to a non-linear vehicle model. Yaw rate and lateral acceleration are observed by physical measurements and estimated using the non-linear vehicle model based on an assumed road condition. A difference between the observed and estimated quantities is used to adjust the non-linear vehicle model, i.e., change the road condition assumption, in order to cause the observed and estimated quantities to more nearly coincide.
The present invention, generally speaking, provides an improved estimator for road/tire friction. The friction estimator provides near-real-time friction estimation, even while the car is accelerating, braking, coasting or turning. It is desirable to have an instantaneous and continuous estimate of the road/tire friction, but an estimate that occurs over several wheel rotations is more realistic. The estimate relies on easily measured signals such as yaw rate, steering wheel angle, lateral acceleration, wheel speed, etc. The estimate can be used to give the driver or a closed-loop controller an advanced warning when the tire force limit is being approached.